Search Results (400)

The equitable allocation of health resources is fundamental to building healthy cities and advancing sustainable regional development. Using panel data for 122 county-level units in Hunan Province, China, from 2011 to 2022, this study constructs an evaluation index system for healthcare resource allocation and applies trend surface analysis, spatial autocorrelation, the Dagum Gini coefficient, and geographically and temporally weighted regression (GTWR) to examine the spatiotemporal evolution, equity, and driving factors of health resource allocation. The results show the following. (1) The level of health resource allocation in Hunan Province rose steadily, with the composite score increasing by 74%, yet a persistent spatial pattern of higher allocation in the east and north than in the west and south remained; hot spots clustered in Changsha, while cold spots concentrated in parts of Southern Hunan and Western Hunan. (2) Regional disparities narrowed gradually, and the Dagum decomposition identified transvariation density as the dominant source of inequality, with an average contribution of 53.58%, exceeding intra-group and inter-group differences. (3) The effects of the drivers exhibited marked spatiotemporal heterogeneity. Per capita GDP promoted health resource allocation mainly in developed regions, while urbanization exerted stronger positive effects in less-developed regions. The positive effect of per capita disposable income gradually shifted from less-developed to developed regions over time, and population density generally showed a positive effect, with stronger influences concentrated in the Greater Western Hunan region. This study contributes to a deeper understanding of how regional disparities and heterogeneous driving mechanisms shape health resource allocation, providing evidence for more adaptive and equitable healthcare governance. Full article

Sudan faces riverine flooding along the Blue Nile and chronic drought across Darfur–Kordofan, yet no national assessment integrates both hazards with social vulnerability to support sustainable and climate-resilient development. This study develops a Societal Impact Index (SII) for Sudan’s eighteen states using a terrain-based flood-susceptibility surface, a drought-frequency indicator (SPEI-12), and thirteen social-vulnerability indicators. These are combined into four weighted pillars following the Intergovernmental Panel on Climate Change (IPCC) risk architecture and validated against independent humanitarian-needs assessments, with convergent checks based on displacement and malnutrition. An unsupervised machine-learning audit, combining k-means clustering with principal component analysis, tests whether the data’s structure supports the composite ranking. The audit shows that the five High-impact states follow two distinct pathways: hazard-and-exposure dominance in Al Qadarif and Al Jazirah, and sensitivity dominance in the remaining three Darfur states. This distinction enables risk-reduction and infrastructure measures to be tailored to the dominant pathway in each state. The first two principal components correlate only weakly with the SII ($r=0.02$ and $r=0.36$), indicating that the ranking reflects the assigned weights as well as the data structure. Each score is exactly decomposed into pillar contributions, improving transparency, while a prototype scenario tool illustrates practical use. Flood-exposed population increased by 7.4 percent between 2017 and 2020, highlighting the need for continuous updating. The reproducible, open-data framework can support equitable resource allocation, sustainable infrastructure planning, long-term vulnerability reduction, and future disaster-resilience digital twins in data-scarce Sahelian settings. Full article

Refugees and asylum seekers in Germany face significant barriers to accessing routine dental care, leading to untreated conditions that escalate into costly emergency hospital admissions and increased public healthcare expenditures. This study evaluates the economic impact of an NGO-led dental care facility designed to address this critical gap in care for uninsured populations. Using a retrospective cost-effectiveness analysis, we compare three scenarios: (1) the NGO intervention, (2) the “status quo” reliance on emergency care, and (3) a dental clinic arm-based model. We test the hypothesis that NGO-led interventions reduce public healthcare costs by curbing preventable emergency admissions, thereby addressing systemic policy and market failures. Results demonstrate that the NGO facility is a cost-effective solution, generating a return of €0.60 for every euro invested, while the alternative scenarios yielded no financial returns. By providing equitable, preventive dental care, the NGO model reduced emergency admissions by addressing delayed treatment-seeking behaviors and structural access barriers. These findings confirm that NGO-led interventions can mitigate market failures by serving as a Pareto-improving solution, optimizing resource allocation and reducing long-term fiscal burdens. The study underscores the potential of NGOs to complement public health systems in achieving equitable and sustainable healthcare delivery. Policymakers should consider scaling such models to alleviate disparities in underserved populations while curbing avoidable costs linked to emergency care. This research contributes critical evidence for integrating NGO-led initiatives into healthcare strategies, particularly in contexts marked by fragmented access and systemic inefficiencies. Full article

Sustainable urban development increasingly depends on the efficient and equitable provision of municipal infrastructure, and waste bins, as critical end-point facilities in urban solid waste management systems, directly influence waste disposal accessibility, collection efficiency, and service equity. However, existing studies remain limited in achieving effective demand–supply matching, controlling spatial redundancy, and evaluating equity in waste facility allocation. This study developed a multi-source spatial evaluation framework integrating waste generation demand, road accessibility, and building functional characteristics, and proposed a two-stage waste bin allocation approach by coupling Adaptive Non-Maximum Suppression (ANMS) with an improved Coverage Location Problem with Overlap Control (CLPOC) model. The proposed framework first generated a theoretical candidate site pool and applied ANMS to achieve spatially balanced candidate refinement, followed by CLPOC-based optimization to improve service coverage and reduce redundant configurations. The results demonstrated that the optimized scheme substantially reduced redundant facility locations while maintaining high service coverage, thereby improving spatial allocation efficiency. Although urban areas exhibited notable improvements in service equity, the enhancement in mountainous and scenic areas was constrained by terrain conditions and spatial clustering effects. This study provides an efficiency–equity balanced framework for fine-scale waste bin planning in hilly cities, offering methodological support for sustainable urban solid waste management and contributing to the broader goal of sustainable urban development. Full article

Background: Timely hepatitis B vaccine birth-dose (HepBV-BD) vaccination is a cornerstone strategy for preventing mother-to-child transmission of hepatitis B virus (HBV). Although hepatitis B vaccination coverage in China has improved substantially, evidence regarding long-term district-level spatiotemporal inequalities in timely birth-dose vaccination remains limited, particularly in western China. This study assessed spatiotemporal disparities in hepatitis B vaccination coverage across Chongqing, China, from 2016 to 2025. Methods: This ecological descriptive study used district- and county-level vaccination surveillance data from the Chongqing Immunization Information Management System. Temporal trends in HepBV-BD, timely birth-dose (TBD), HepBV2, and HepBV3 coverage were analyzed. Regional disparities, interregional inequalities, and spatial clustering were assessed using Global Moran’s I and Local Indicators of Spatial Association (LISA). Results: All vaccination indicators improved substantially during the study period. TBD coverage increased from 81.9% to 95.4%, whereas HepBV3 coverage increased from 83.7% to 98.7%. TBD coverage was strongly positively associated with HepBV3 coverage(r = 0.95, p < 0.001). Mountainous ethnic minority regions consistently exhibited the lowest TBD coverage despite marked improvement over time. Absolute interregional inequality decreased from 30.8 to 8.4 percentage points. Although Global Moran’s I was not statistically significant, LISA analysis identified persistent localized low-coverage clusters in southeastern mountainous regions and emerging high–high clusters in northern Chongqing. Conclusions: Hepatitis B vaccination coverage in Chongqing improved markedly between 2016 and 2025, accompanied by declining geographic inequality. Nevertheless, persistent spatial inequities in timely birth-dose vaccination remained in mountainous ethnic minority areas. Continued monitoring of spatial inequalities, together with strengthened primary healthcare capacity, culturally tailored health education, and equitable resource allocation, may help support geographically targeted immunization strategies and accelerate progress toward the WHO goal of eliminating viral hepatitis as a public health threat by 2030. Full article

District heating provides efficient urban heating; however, sustainable cost allocation methods in multi-apartment buildings often require additional attention, especially in developing markets such as Prishtina, Kosovo. These systems often rely on flat-rate billing, and they fail to account for heat consumption in common areas. Consequently, not all heat entering the building is accounted for or billed. This leads to systemic cross-subsidization, penalizing energy-conscious consumers while allowing inactive or unoccupied units to benefit from unmetered heat transfer without contributing to the associated costs. To address this, this study proposes a coefficient-based economic allocation model to equitably distribute common area heating costs in mixed-metered buildings, thereby promoting sustainable energy consumption. Using Prishtina as a case study, the methodology determines an optimal common-heat coefficient (β) via constrained boundary analysis based on a physical constraint (net-to-gross area ratio of the building stock ≤ 27.5%) and an economic constraint (fixed charges ≤ 30%). The methodology was informed by a comprehensive dataset of 414 thermal substations provided by the district heating provider “Termokos” (covering 18,546 heating units during the 2024/2025 heating season), with Substation 277-UL presented as a field case study to demonstrate the practical application and validity of the model. The results show three findings: First, the optimal coefficient is established at β = 20%, satisfying both boundaries and showing that the attributed part of the heating consumption for the common area should not surpass this value. Second, the framework tackles cross-subsidization, reducing specific bills for regular, heat cost allocator-metered consumers by 21.7% compared to unmetered billing. Third, the model proposes a transparent approach to distribute common heat costs across the building community, ensuring equitable participation in shared thermal expenses of common areas. Hence, this research serves as a first step toward establishing equitable billing policies, maintaining energy balance, and fostering socio-economic and energy sustainability in the district heating industry. Full article

Disaster preparedness and the equitable distribution of emergency relief supplies remain pressing priorities for governments worldwide. Situated along the Pacific Ring of Fire, Taiwan is particularly vulnerable to natural disasters that frequently cause severe property damage and endanger public safety, making the efficient allocation of limited relief resources in the immediate aftermath a persistent challenge for emergency management. Although prior research has addressed risk assessment and resource allocation as separate problems, few studies have incorporated region-specific social vulnerability values directly into an optimization framework for relief distribution. This raises the question of how region-specific social vulnerability can be quantitatively embedded into a relief-supply allocation model to achieve a more equitable, risk-sensitive distribution than conventional population-based approaches. To address this gap, the present study develops an integrated decision-making model combining a fuzzy inference system (FIS) with fuzzy multiple objective linear programming (FMOLP). The FIS first derives social risk values from four vulnerability dimensions, namely exposure, disaster mitigation and preparedness, readiness, and recovery, for Taiwan’s 17 counties and municipalities, drawing on multidimensional indicators from the National Science and Technology Center for Disaster Reduction (NCDR) Disaster Mitigation Database. These values are subsequently incorporated as weighting coefficients within the FMOLP model, which jointly maximizes distributional utility and minimizes procurement cost under population-based supply constraints. The results identify Hsinchu, Taichung, Chiayi, Yunlin, and Hualien as the five highest-risk regions. The model further yields a compromise allocation plan spanning all 17 administrative units, with sensitivity analysis confirming its robustness across alternative performance metrics. This study offers a replicable, data-driven decision-support tool to assist disaster preparedness planners and government agencies in relief resource allocation. Full article

Over the past three decades, the United States has experienced a notable increase in weather-related disasters, including hurricanes, floods, tornadoes, wildfires, and severe storms, posing growing challenges to healthcare preparedness and public health systems. This study analyzes Federal Emergency Management Agency (FEMA) disaster declarations from 1989 to 2019 to characterize temporal and geographic trends in weather-related events. Data after 2019 were excluded to avoid confounding effects associated with the COVID-19 pandemic, which disrupted disaster declarations, resource allocation, and healthcare system demands. Using descriptive statistics, generalized linear mixed models, and spatial clustering techniques, we identified substantial increases and nonlinear patterns in disaster declarations, with variation across hazard types and regions. These trends reflect evolving hazard exposure, regional differences, and policy-driven declaration practices. Although this study does not directly measure health outcomes or social vulnerability, the observed patterns have important implications for healthcare system capacity, workforce preparedness, and populations known to be disproportionately affected by disasters. The findings highlight the need for climate-informed training, data-driven preparedness planning, and integration of disaster trend analysis into nursing education and public health practice. Strengthening the ability of healthcare systems to anticipate and respond to evolving disaster patterns is critical for advancing resilience and promoting equitable health outcomes in the context of climate change. Full article

Coordinated management of drip irrigation water demand and lateral canal supply is a critical strategy for improving water use efficiency in arid irrigation districts; however, under water-deficit conditions, the efficient and equitable allocation of limited canal water among multiple drip irrigation systems remains largely unresolved. This study developed a bi-objective cooperative water allocation and scheduling model for a lateral canal serving 11 subordinate drip irrigation systems. Subject to canal diversion flow balance and total deficit constraints, the model simultaneously minimized (i) the mean coefficient of variation (CV) of water allocation duration within rotation irrigation groups, targeting temporal uniformity, and (ii) the sum of squared deviations of the water supply satisfaction rate across systems, targeting distributional equity. Water demand inputs were derived from a localized FAO-56 Penman–Monteith irrigation schedule for Jinghe County with stage-specific crop coefficients. A hybrid binary–continuous NSGA-II encoding with a dynamic intra-group flow allocation mechanism was employed. For the baseline deficit scenario (Early May, supply-to-demand ratio β = 67.76%), the model partitioned the 11 systems into four rotation groups with a mean CV of 3.15 × 10⁻³, while the sum of squared deviations of the satisfaction rate decreased from 4.366 under the empirical scheme to 1.50 × 10⁻⁴, confining all systems to 67.38–68.45% and eliminating the coexistence of over-supply and complete deprivation (Wilcoxon signed-rank test, p < 0.001; Cohen’s d = −1.698). The Pareto front revealed a significant efficiency–equity trade-off (Spearman’s ρ = −0.9999), and NSGA-II outperformed SPEA2 by approximately 29-fold and 22-fold in the two objectives. Robustness was confirmed across three deficit scenarios and algorithm parameter sensitivity analyses (CV < 2%). The study offers methodological support for refined water allocation management of terminal canal systems in arid regions. Full article

Equity is fundamental to an effective pandemic vaccination response, yet the COVID-19 pandemic demonstrated how inequitable access, driven by purchasing power and delayed supply for low- and middle-income countries (LMICs), prolonged transmission and enabled viral evolution. The Pandemic Influenza Preparedness (PIP) Framework offers a model for providing timely and equitable access to pandemic influenza vaccines. Through legally binding advance supply agreements, the PIP Framework secures real-time access by the WHO to a proportion of global vaccine production and mandates allocation based on public health risk and need. In doing so, it addresses the two axes of inequity observed in previous pandemics—distribution and timeliness—by ensuring that affected populations that do not have access, irrespective of income status, are not at the back of the queue. Full article

Background/objectives: As Guangzhou’s population ages rapidly, the supply-and-demand gap in elderly care facilities has become severe. We propose a ‘4A’ model that draws on accessibility, affordability, acceptability, and availability from the classic ‘5A’ access framework to optimize nursing home bed allocation from existing facilities. Methods: We applied the ‘4A’ model to 2630 communities/villages and 243 nursing homes in Guangzhou, using linear programming to maximize a matching score under capacity, demand, and occupancy constraints. Inequality was assessed using the Gini coefficient, Theil index, Moran’s I, and hot spot analysis. Four policy scenarios were simulated under resource-scarce and resource-abundant conditions. Results: A total of 753 (28.6%) communities/villages had no access to nursing homes. The unmet demand rates followed a ‘dual-center’ clustering pattern, and hot spots were not only in the urban core, where ‘scale disadvantage’ and ‘matching disadvantage’ coexisted, but also in outer suburban districts to form a ‘supply vacuum.’ Inequity was moderate, and it originated from between-district disparities. Compared with a ‘distance-only variant’ model, our ‘4A’ model allocated 3813 more beds. Policy simulations showed that minimum service ($\alpha =0.1$) eliminated all unserved communities/villages; targeted bed expansion increased satisfaction rates to 74.8% and reduced the Gini coefficient to 0.280; and subsidies and quality upgrades became effective only when bed supply was abundant. The four simulations were phased into a three-phase policy roadmap. Conclusions: The ‘4A’ model transforms the qualitative ‘5A’ framework into a computable allocation matrix, offering actionable recommendations for equitable access to elderly care resources. Full article

Background: Equitable and adequate access to primary care services is essential for reducing healthcare disparities and advancing social justice. In developing countries like China, achieving a balanced primary care provision across urban–rural divides, regions, and population groups represents a critical strategy for improving public health outcomes. Methods: This study integrates high-resolution population data, nationwide point of interest (POI) data, and aggregated individual survey data to analyze the spatial distribution of primary care facilities in China, evaluate their accessibility and equity, and examine the relationships among primary care accessibility, socioeconomic factors, and public health outcomes using geographic analysis and machine-learning methods. Results: (1) Primary care facilities in China exhibit significant spatial clustering and pronounced urban–rural disparities, with 23% of the urban population having access within walking distance; (2) while horizontal equity in primary care accessibility is relatively well-maintained for China’s aging population, vertical equity requires substantial improvement; and (3) primary care accessibility demonstrates significant but nonlinear associations with key socioeconomic indicators, including urban population size, GDP, built-up area, health insurance coverage, and public expenditure. Conclusions: These findings provide valuable insights for health resource allocation and urban planning policies aimed at achieving equitable primary care access. Full article

Background: Equitable allocation of public health budgets across multiple intervention domains remains a major challenge in regional health governance. In Thailand’s Health Region 10, annual healthcare budgets must address diverse health burdens across several provinces, while current planning approaches rely on expert deliberation and historical precedent without systematic exploration of alternative allocation strategies. Public health resource allocation decisions are inherently multi-criteria, integrating health impact, cost-effectiveness, equity, disease severity, clinical and ethical priorities, feasibility, and alignment with national health policy agendas—dimensions that cannot be reduced to a single metric. This study introduces H-RL-MUSYA (Hierarchical Reinforcement Learning for Multi-Domain Unified System of Yielding Adaptive allocations), a decision-support framework designed to assist—not replace—public health practitioners by systematically generating and evaluating a menu of Pareto-efficient allocation strategies across four priority domains: nutrition, mental health, behavioral risk, and accident prevention. The framework explicitly acknowledges that DALYs averted and cost-effectiveness ratios are valuable but partial indicators, and that final resource allocation must integrate additional considerations—including underpinning health policies, priority population needs, feasibility, and contextual judgment—that lie beyond the model’s scope. Results: Applied to Thailand’s Health Region 10 (4.6 million inhabitants), H-RL-MUSYA identified 127 Pareto-efficient policies yielding a representative compromise allocation that averted 847,293 DALYs (34.1% improvement over historical allocations), improved cost-effectiveness by 31.3%, and reduced the health equity Gini coefficient from 0.243 to 0.187. A 12-month prospective pilot confirmed +23.1% composite health improvement with 91% stakeholder acceptance. Conclusions: H-RL-MUSYA demonstrates that AI-assisted policy exploration can meaningfully enrich public health decision-making by surfacing non-intuitive allocation strategies and quantifying equity–efficiency trade-offs, while human expertise, policy context, and democratic deliberation remain essential for final allocation decisions. Full article

Establishing an equitable, evidence-based mechanism for allocating flood prevention funding is critical to mitigating the risk of flash floods. However, existing research seldom accounts for the multi-dimensional nature of vulnerability or achieves an appropriate balance between efficiency and equity. To address this gap, we propose the Multi-dimensional Vulnerability-based Flood Disaster Fund Allocation Optimization Model (MD-FAOM), which integrates the coupling effects of exposure, sensitivity, adaptive capacity, and equity into allocation strategies using the NSGA-II algorithm, TOPSIS method, and geographical detectors. The model prioritizes funding for ecologically targeted flood prevention. We apply this framework to southern Tibet to derive optimal fund allocations and quantitatively assess the resulting benefits. Our results show that areas characterized by negative vulnerability account for 25.22% of the study region, mainly concentrated in Lhasa and Shannan. Under equivalent conditions, MD-FAOM delivers benefits across an area of 85,915 km², achieving an improvement rate of 30.11%. These findings demonstrate that integrating vulnerability science with distributive equity can optimize the allocation of limited resources, thereby enhancing both flood resilience and ecosystem conservation. This approach advances ecohydrological disaster management and supports the achievement of Sustainable Development Goals (SDGs) 13 (Climate Action) and 15 (Life on Land). Full article

The 15-minute life circle has become a critical planning paradigm for developing age-friendly cities; however, its implementation in small and medium-sized cities (SMSCs) is often constrained by topographical heterogeneity and the oversimplified use of facility density as a proxy for service efficiency. This study challenges the conventional assumption that a higher facility density automatically leads to better service outcomes and proposes a dynamic “space–demand–policy” analytical framework to identify the mechanisms underlying spatial mismatch. Using Zhifu District, Yantai, a rapidly aging urban area with complex topography, as a case study, we integrated kernel density estimation (KDE), slope-adjusted network analysis, a modified Gaussian-based two-step floating catchment area (2SFCA) method, and standard deviational ellipse (SDE) analysis. Our results reveal three key findings. First, a “high-density, low-efficiency” paradox is prevalent: older urban cores contain clusters of facilities but have supply–demand ratios below 0.5 because of limited service diversity and slope-induced reductions in accessibility, with the effective service radius decreasing to 750 m. Second, newly developed areas achieve service coverage rates below 50% when terrain constraints are considered, highlighting the limitations of static planning radii. Third, a 90% overlap between the directional distributions of residential areas and elderly care facilities, as indicated by their SDE major axes, supports the spatial feasibility of community-embedded aging-in-place models; however, physical proximity alone does not guarantee effective service delivery. By adapting generic spatial algorithms to create a terrain-sensitive planning tool, this study provides a transferable framework for evidence-based and targeted elderly care planning in SMSCs facing similar demographic and geographic constraints. The proposed framework contributes to the global sustainability agenda and advances SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being) by promoting more equitable resource allocation and supporting the development of age-friendly, walkable, and sustainable communities in topographically complex urban areas. Full article